Aligning LLMs for Multilingual Consistency in Enterprise Applications

• URL: http://arxiv.org/abs/2509.23659v2
• Date: Sat, 25 Oct 2025 18:56:44 GMT
• Title: Aligning LLMs for Multilingual Consistency in Enterprise Applications
• Authors: Amit Agarwal, Hansa Meghwani, Hitesh Laxmichand Patel, Tao Sheng, Sujith Ravi, Dan Roth,
• Abstract summary: Large language models (LLMs) remain unreliable for global enterprise applications.<n>We propose a practical, batch-wise alignment strategy for fine-tuning LLMs.<n>This approach improves non-English accuracy by up to 23.9% without compromising English performance, model reasoning, or retrieval quality.
• Score: 39.24743635297897
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models (LLMs) remain unreliable for global enterprise applications due to substantial performance gaps between high-resource and mid/low-resource languages, driven by English-centric pretraining and internal reasoning biases. This inconsistency undermines customer experience and operational reliability in multilingual settings such as customer support, content moderation, and information retrieval. Even with advanced Retrieval-Augmented Generation (RAG) systems, we observe up to an 29% accuracy drop in non-English languages compared to English. We propose a practical, batch-wise alignment strategy for fine-tuning LLMs, leveraging semantically equivalent multilingual data in each training batch to directly align model outputs across languages. This approach improves non-English accuracy by up to 23.9% without compromising English performance, model reasoning, or retrieval quality. Our method is simple to implement, scalable, and integrates seamlessly with existing LLM training & deployment pipelines, enabling more robust and equitable multilingual AI solutions in industry.

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